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Open Access

CRESP: Cost-Aware Recommendation-Oriented Edge Service Provision

School of Information Technology, Jiangsu Open University, Nanjing 210036, China
College of Arts, Business, Law, Education and IT, Victoria University, Melbourne 3011, Australia
School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
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Abstract

In the 5G environment, the edge computing paradigm enables service providers to deploy their service instances on distributed edge servers to serve nearby end users with extremely low latency. This boosts the emergence of modern applications, like AR/VR, online gaming, and autonomous vehicles. Existing approaches find service provision strategies under the assumption that all the user requirements are known. However, this assumption may not be true in practice and thus the effectiveness of existing approaches could be undermined. Inspired by the great success of recommender systems in various fields, we can mine users’ interests in new services based on their similarities in terms of current service usage. Then, new service instances can be provisioned accordingly to better fulfil users’ requirements. We formulate the problem studied in this paper as a Cost-aware Recommendation-oriented Edge Service Provision (CRESP) problem. Then, we formally model the CRESP problem as a Constrained Optimization Problem (COP). Next, we propose CRESP-O to find optimal solutions to small-scale CRESP problems. Besides, to solve large-scale CRESP problems efficiently, we propose an approximation approach named CRESP-A, which has a theoretical performance guarantee. Finally, we experimentally evaluate the performance of both CRESP-O and CRESP-A against several state-of-the-art approaches on a public testbed.

Keywords

Edge Computing (EC)edge services recommendationedge service deploymentservice provider

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Tsinghua Science and Technology
Volume 30 Issue 4,
August 2025
Pages 1865-1884
DOI: 10.26599/TST.2024.9010151
Cite this article:
Huang L, Li B, Zhao L. CRESP: Cost-Aware Recommendation-Oriented Edge Service Provision. Tsinghua Science and Technology, 2025, 30(4): 1865-1884. https://doi.org/10.26599/TST.2024.9010151

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Received: 28 May 2024
Revised: 13 August 2024
Accepted: 16 August 2024
Published: 03 March 2025
© The Author(s) 2025.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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